Distributed route planning for multiple mobile robots using an augmented Lagrangian decomposition and coordination technique

Nishi, Tatsushi; Ando, Masakazu; Konishi, Masami

doi:10.1109/tro.2005.853489

Cited by 48 publications

(23 citation statements)

References 20 publications

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“…The line passing through Cx 1 i and Cx 2 i may intersect with the ellipsoid, which indicates that collision avoidance constraints are not convex. Compared with the formulation in [1], [3], [12], the collision avoidance constraints in this paper are formulated as general non-convex constraints, which will be used in the next section to demonstrate the general scheme of convexifying non-convex polynomial constraints.…”

Section: Problem Formulationmentioning

confidence: 99%

“…For future systems with a large number of cooperative agents such as ground vehicles, unmanned aerial vehicles (UAVs) and satellites, it is either necessary or highly demanded to develop distributed planning algorithms [1], [2], [3]. In this paper we present a general framework to address distributed planning for formation reconfiguration problems.…”

Section: Introductionmentioning

confidence: 99%

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Distributed planning of optimal reconfiguration with collision avoidance and final configuration constraints

Guo

Jing

Yan

2015

2015 54th IEEE Conference on Decision and Control (CDC)

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This paper presents a distributed framework to develop planning algorithms for optimal reconfiguration of multiagent systems in the presence of collision avoidance and final configuration constraints. First, reconfiguration problems are formulated as optimal control problems which are addressed using direct methods. Thus, reconfiguration problems become nonlinear programming problems subject to coupling variables, i.e., final configuration constraints, and coupling constraints, i.e., collision avoidance constraints. In our framework subgradient methods are adopted to include reconfiguration cases with non-differentiable objectives. Then, to develop distributed algorithms, final configuration constraints are tackled by primal decomposition, while collision avoidance constraints by dual decomposition. Since standard decomposition methods prevent the distributed implementation due to the existence of master problems, primal decomposition is combined with the distributed consensus algorithm and dual decomposition is integrated with the incremental subgradient method. In the end, this framework is employed to develop distributed planning algorithms for optimal reconfiguration of satellite clusters.

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Section: Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Distributed planning of optimal reconfiguration with collision avoidance and final configuration constraints

Guo

Jing

Yan

2015

2015 54th IEEE Conference on Decision and Control (CDC)

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“…(12). Given the allocation kind number p (here, it is equal to robot number), set iteration threshold ε > 0, and the max-iteration number MAX_NUM.…”

Section: Exploration Mission Planning Descriptionsmentioning

confidence: 99%

“…Reference [11] brought forward a neural network algorithm to minimize the longest path, but the model parameters are complex. An augmented Lagrangian decomposition [12] and coordination technique are used to minimize the total transportation time. Reference [13] proposed an autonomous decentralized method for multiple automated guided vehicles (AGVs) static route planning.…”

Section: Introductionmentioning

confidence: 99%

Multi-robot exploration mission planning and stochastic increment replanning for load balance

Cai

2010

Front. Electr. Electron. Eng. China

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Multi-robot mission planning is composed of assignment allocation and mobile-robot route planning in this paper. Multi-robot exploration missions adopts fuzzy c-mean (FCM) algorithm to allocate, and then, heterogeneous interactive cultural hybrid algorithm (HICHA) is devised for route planning in order to optimize mobilerobot execution path. Meanwhile, we design multi-robot mission replanning mechanism based on the rules system of greedy algorithm for dynamic stochastic increment missions. Finally, extensive simulation experiments were shown that FCM for assignment allocation and HICHA for route planning were efficacious for mobile-robot exploration mission planning. Furthermore, the improved greedy algorithm based on experience rules met dynamic stochastic increment missions replanning requirement for load balance.

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“…The parameters for the algorithm are skipping probability r and step size of penalty coefficient Δω. The tuning method for these parameters are shown in [1].…”

Section: B Decomposition and Optimization Of Petri Netsmentioning

confidence: 99%

Decomposition of Petri Nets for Optimization of Routing Problem for AGVs in Semiconductor Fabrication Bays

Nishi

Maeno

2006

2006 IEEE International Conference on Automation Science and Engineering

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In this paper, we propose a decomposition and optimization method for Petri Nets to solve routing problems for automated guided vehicles in semiconductor fabrication bays. An augmented Petri Net model is developed to represent concurrent motion for multiple AGVs. The routing problem to minimize total transportation time is formulated as an optimal firing sequence problem for the proposed Petri Net. The optimization model is decomposed into several subproblems which can be solved by Dijkstra's algorithm in polynomial order. The effectiveness of the proposed method is evaluated by several numerical examples.

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Distributed route planning for multiple mobile robots using an augmented Lagrangian decomposition and coordination technique

Cited by 48 publications

References 20 publications

Distributed planning of optimal reconfiguration with collision avoidance and final configuration constraints

Distributed planning of optimal reconfiguration with collision avoidance and final configuration constraints

Multi-robot exploration mission planning and stochastic increment replanning for load balance

Decomposition of Petri Nets for Optimization of Routing Problem for AGVs in Semiconductor Fabrication Bays

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